How do you define mass shootings?

Our data source, The Violence Project, defines a public mass shooting as follows:

“a multiple homicide incident in which four or more victims are murdered with firearms—not including the offender(s)—within one event, and at least some of the murders occurred in a public location or locations in close geographical proximity (e.g., a workplace, school, restaurant, or other public settings), and the murders are not attributable to any other underlying criminal activity or commonplace circumstance (armed robbery, criminal competition, insurance fraud, argument, or romantic triangle).”

Which mass shootings did you include?

We included all 60 mass shootings in the United States from April 2013 to March 2023 (The Violence Project). You can learn more about them in the table below. Click on a column title to sort by it.

use_subreddit = name of the subreddit used for the analysis. n_posts = number of Reddit comments containing at least 15 words. n_authors = number of unique authors. min_date = date of earliest Reddit post used (set to 6 weeks before the event). max_date = date of latest Reddit post used (set to 6 weeks after the event).

Below is a map of the mass shootings included in our analysis. Each circle is a mass shooting, and its radius is proportional to the number of people killed and the number of people injured.

What are federally declared natural disasters?

When a disaster hits a state, the governor of said state can request for a declaration by the President. The declaration paves way for the state to receive federal assistance in recovering from the disaster. For more information on how disasters get declared, check out this video from Federal Emergency Management Agency.

Which disasters did you include?

We included all 196 federally declared disasters in the United States from April 2013 to March 2023 (FEMA).

In cases where a disaster was declared both as an emergency and a major disaster (these are the two declaration types), we only kept one observation.

Since each disaster declaration is filed by a state, you may see the same disaster multiples times if multiple states filed requests.

You can learn more about the disasters included in the table below. Click on a column title to sort by it.

use_subreddit = name of the subreddit used for the analysis. n_posts = number of Reddit comments containing at least 15 words. n_authors = number of unique authors. min_date = date of earliest Reddit post used (set to 6 weeks before the event). max_date = date of latest Reddit post used (set to 6 weeks after the event).

This map might help you situate the disasters. Each circle is a disaster, and its radius is proportional to the dollar amount of federal grant given to assist in recovery from the disaster.

Not all upheavals are created equal… have you thought about how variations in the severity of the upheavals may affect your results?

We think this is a super important point! In fact, we tried to redo our analysis using some proxies for severity. The results in general look like what you would predict: more severe upheavals saw larger effects. But there are exceptions. Check them out for yourself in the plots below.

Which severity proxies did you use?

For mass shootings, we used the number of people killed + the number of people injured. For disasters, we used the total amount of federal grant given to an affected region.

These are admittedly very crude proxies. For example, the number of people affected by the disasters would be a better way to make the proxy for disasters comparable to that for the mass shootings. The proxies we used also don’t really get at how severe people think these upheavals are. We think perceived severity probably affects language use more than actual severity. It may be better captured by something like the amount of media coverage/some measure of how much people talked about the upheavals on-/off-line.

I have a better proxy/way of running this analysis in mind!

We would be so appreciative if you could share it in this form!

Show me the plots!

Mass shootings

Disasters

What about time? You cover a 10-year span in your analysis. Maybe something has changed over time in how people react to these upheavals…

We think you’re totally onto something! Just anecdotally, we have found seeing distressing news every day making us less able/willing to grasp just how distressing these events actually are. Overtime we seem to be slowly but surely being desensitized to things that should have drawn out more reactions from us.

We broke down the analysis into three time bins in the plots below. This is obviously super crude, and we’d love to hear that better idea forming in your head right now: please share it in this (form)[URL NEEDED]!

Show me the plots!

Mass shootings

Disasters

I like plots and stuff, but you haven’t run any statistical tests? How can I know if this is true?

Ha yes—in this poster we’ve erred toward the side of simplicity. You’re absolutely right that your eyes can trick you into seeing something that Does Not Exist. Although your stats can probably do a very good job at that, too.

Anyhow, we ran a few paired t-tests (again doing the simplest thing possible). Here’s how we did it: * We took the mean of linguistic measures from 6 weeks to 2 weeks before the upheaval as baseline. Each author gets their own baseline. * For every week-long period from 1 week before the upheaval to 6 weeks after the upheaval, we compared the author’s linguistic measures in that period to those in their baseline. * If there lacks a statistically significant (we set the bar at the 3% level) and substantially meaningful (well, this is up to your interpretation—we’re again reminding you that all measures, except for Analytic, are actual % of words in the text), then we say that the person has reverted back to their baseline. * Note: week 0 is the day of the upheaval.

There’re certainly better ways to do it! Pplease do share your thoughts in this (form)[URL NEEDED].

t-tests

Shootings

Characteristic -1 wk, N = 26,9621 baseline, N = 26,9621 Difference2 95% CI2,3 p-value2
i 3.57 (3.48) 3.62 (3.01) -0.05 -0.10, 0.00 0.042
we 0.54 (1.37) 0.52 (1.09) 0.02 0.00, 0.04 0.031
they 1.37 (2.09) 1.40 (1.75) -0.02 -0.05, 0.01 0.2
Analytic 47 (26) 48 (22) -0.71 -1.1, -0.32 <0.001
cogproc 12.4 (5.9) 12.2 (4.9) 0.17 0.09, 0.26 <0.001
prosocial 0.47 (1.18) 0.46 (0.96) 0.01 -0.01, 0.03 0.2
emo_anx 0.08 (0.47) 0.09 (0.41) -0.01 -0.01, 0.00 0.15
emo_anger 0.15 (0.64) 0.14 (0.51) 0.01 0.00, 0.02 0.059
emo_sad 0.07 (0.44) 0.07 (0.36) 0.00 -0.01, 0.01 0.6
1 Mean (SD)
2 Paired t-test
3 CI = Confidence Interval
Characteristic 0 wk, N = 25,6711 baseline, N = 25,6711 Difference2 95% CI2,3 p-value2
i 3.54 (3.49) 3.58 (2.97) -0.05 -0.10, 0.00 0.068
we 0.56 (1.38) 0.52 (1.12) 0.04 0.02, 0.06 <0.001
they 1.39 (2.11) 1.40 (1.73) -0.02 -0.05, 0.02 0.3
Analytic 47 (26) 48 (22) -0.52 -0.91, -0.12 0.011
cogproc 12.4 (5.9) 12.2 (4.9) 0.15 0.06, 0.24 0.001
prosocial 0.45 (1.15) 0.45 (0.98) 0.00 -0.02, 0.02
0.9
emo_anx 0.08 (0.45) 0.08 (0.40) 0.00 -0.01, 0.00 0.2
emo_anger 0.15 (0.64) 0.14 (0.53) 0.01 0.00, 0.02 0.020
emo_sad 0.07 (0.45) 0.07 (0.36) 0.00 0.00, 0.01 0.4
1 Mean (SD)
2 Paired t-test
3 CI = Confidence Interval
Characteristic 1 wk, N = 25,8941 baseline, N = 25,8941 Difference2 95% CI2,3 p-value2
i 3.51 (3.44) 3.61 (3.04) -0.10 -0.15, -0.05 <0.001
we 0.57 (1.38) 0.53 (1.11) 0.05 0.02, 0.07 <0.001
they 1.37 (2.03) 1.39 (1.76) -0.02 -0.05, 0.01 0.2
Analytic 46 (26) 47 (22) -1.1 -1.5, -0.68 <0.001
cogproc 12.6 (5.9) 12.2 (4.9) 0.35 0.26, 0.44 <0.001
prosocial 0.49 (1.19) 0.46 (0.98) 0.04 0.02, 0.05 <0.001
emo_anx 0.10 (0.51) 0.08 (0.39) 0.01 0.01, 0.02 0.001
emo_anger 0.16 (0.69) 0.14 (0.54) 0.02 0.01, 0.04 <0.001
emo_sad 0.10 (0.53) 0.07 (0.38) 0.03 0.02, 0.04 <0.001
1 Mean (SD)
2 Paired t-test
3 CI = Confidence Interval
Characteristic 2 wk, N = 23,9331 baseline, N = 23,9331 Difference2 95% CI2,3 p-value2
i 3.61 (3.49) 3.61 (3.01) 0.01 -0.05, 0.06 0.8
we 0.52 (1.32) 0.52 (1.09) 0.00 -0.02, 0.02 0.7
they 1.44 (2.14) 1.38 (1.72) 0.06 0.03, 0.09 <0.001
Analytic 47 (26) 48 (22) -0.41 -0.82, 0.01 0.054
cogproc 12.4 (5.9) 12.2 (4.9) 0.15 0.06, 0.24 0.002
prosocial 0.46 (1.19) 0.46 (1.00) 0.00 -0.02, 0.02 0.9
emo_anx 0.08 (0.45) 0.08 (0.39) 0.00 -0.01, 0.01 0.6
emo_anger 0.14 (0.61) 0.13 (0.48) 0.01 0.00, 0.02 0.041
emo_sad 0.07 (0.45) 0.07 (0.36) 0.00 -0.01, 0.01 0.6
1 Mean (SD)
2 Paired t-test
3 CI = Confidence Interval
Characteristic 3 wk, N = 23,0131 baseline, N = 23,0131 Difference2 95% CI2,3 p-value2
i 3.57 (3.47) 3.60 (2.99) -0.03 -0.08, 0.02 0.3
we 0.54 (1.36) 0.52 (1.10) 0.01 -0.01, 0.03 0.3
they 1.44 (2.15) 1.39 (1.76) 0.05 0.02, 0.09 0.002
Analytic 47 (26) 48 (22) -0.44 -0.86, -0.02 0.041
cogproc 12.3 (5.9) 12.2 (4.9) 0.07 -0.02, 0.17 0.12
prosocial 0.46 (1.16) 0.45 (0.96) 0.01 -0.01, 0.03 0.2
emo_anx 0.08 (0.43) 0.08 (0.39) -0.01 -0.02, 0.00 0.030
emo_anger 0.13 (0.59) 0.13 (0.50) 0.00 -0.01, 0.01 0.6
emo_sad 0.07 (0.45) 0.07 (0.36) 0.00 -0.01, 0.00 0.4
1 Mean (SD)
2 Paired t-test
3 CI = Confidence Interval
Characteristic 4 wk, N = 22,2311 baseline, N = 22,2311 Difference2 95% CI2,3 p-value2
i 3.52 (3.45) 3.62 (3.02) -0.10 -0.15, -0.04 <0.001
we 0.51 (1.29) 0.52 (1.09) -0.01 -0.03, 0.01 0.4
they 1.43 (2.12) 1.39 (1.73) 0.04 0.01, 0.08 0.025
Analytic 48 (26) 48 (22) -0.15 -0.58, 0.28 0.5
cogproc 12.3 (5.8) 12.2 (4.9) 0.08 -0.01, 0.18 0.086
prosocial 0.46 (1.15) 0.46 (1.05) 0.00 -0.02, 0.02
0.9
emo_anx 0.08 (0.43) 0.09 (0.41) -0.01 -0.02, 0.00 0.017
emo_anger 0.14 (0.61) 0.13 (0.48) 0.01 0.00, 0.02 0.062
emo_sad 0.07 (0.46) 0.07 (0.38) -0.01 -0.01, 0.00 0.2
1 Mean (SD)
2 Paired t-test
3 CI = Confidence Interval
Characteristic 5 wk, N = 21,9921 baseline, N = 21,9921 Difference2 95% CI2,3 p-value2
i 3.57 (3.53) 3.62 (3.00) -0.04 -0.10, 0.01 0.2
we 0.55 (1.38) 0.50 (1.07) 0.04 0.02, 0.06 <0.001
they 1.39 (2.12) 1.41 (1.76) -0.02 -0.05, 0.02 0.4
Analytic 48 (26) 48 (22) 0.09 -0.35, 0.52 0.7
cogproc 12.2 (5.8) 12.2 (4.9) -0.03 -0.13, 0.07 0.5
prosocial 0.45 (1.13) 0.44 (0.96) 0.00 -0.02, 0.02 0.8
emo_anx 0.07 (0.44) 0.08 (0.37) -0.01 -0.01, 0.00 0.11
emo_anger 0.14 (0.65) 0.13 (0.49) 0.01 0.00, 0.02 0.079
emo_sad 0.08 (0.49) 0.07 (0.35) 0.01 0.00, 0.01 0.15
1 Mean (SD)
2 Paired t-test
3 CI = Confidence Interval
Characteristic 6 wk, N = 19,9011 baseline, N = 19,9011 Difference2 95% CI2,3 p-value2
i 3.6 (3.5) 3.6 (3.0) 0.00 -0.06, 0.06
0.9
we 0.53 (1.37) 0.51 (1.05) 0.03 0.00, 0.05 0.027
they 1.40 (2.09) 1.38 (1.72) 0.02 -0.02, 0.06 0.3
Analytic 47 (27) 48 (22) -0.33 -0.79, 0.12 0.2
cogproc 12.2 (6.0) 12.1 (4.9) 0.07 -0.03, 0.18 0.15
prosocial 0.44 (1.15) 0.45 (0.98) -0.01 -0.03, 0.01 0.3
emo_anx 0.07 (0.43) 0.08 (0.40) -0.01 -0.02, 0.00 0.004
emo_anger 0.14 (0.65) 0.13 (0.49) 0.01 0.00, 0.02 0.12
emo_sad 0.07 (0.45) 0.07 (0.37) 0.00 -0.01, 0.01 0.8
1 Mean (SD)
2 Paired t-test
3 CI = Confidence Interval

Disasters

Characteristic -1 wk, N = 81,0611 baseline, N = 81,0611 Difference2 95% CI2,3 p-value2
i 3.59 (3.52) 3.59 (2.98) 0.00 -0.03, 0.02 0.8
we 0.54 (1.32) 0.55 (1.12) -0.01 -0.03, 0.00 0.018
they 1.42 (2.15) 1.41 (1.74) 0.00 -0.02, 0.02 0.8
Analytic 47 (26) 47 (22) -0.03 -0.25, 0.20 0.8
cogproc 12.3 (5.9) 12.3 (4.9) 0.03 -0.02, 0.08 0.2
prosocial 0.47 (1.16) 0.47 (0.98) 0.00 -0.01, 0.01
0.9
emo_anx 0.08 (0.46) 0.09 (0.39) 0.00 -0.01, 0.00 0.3
emo_anger 0.14 (0.63) 0.14 (0.50) 0.00 0.00, 0.01 0.5
emo_sad 0.08 (0.46) 0.07 (0.36) 0.00 0.00, 0.01 0.2
1 Mean (SD)
2 Paired t-test
3 CI = Confidence Interval
Characteristic 0 wk, N = 81,0861 baseline, N = 81,0861 Difference2 95% CI2,3 p-value2
i 3.6 (3.5) 3.6 (3.0) 0.02 -0.01, 0.05 0.14
we 0.57 (1.37) 0.54 (1.12) 0.02 0.01, 0.03 <0.001
they 1.40 (2.12) 1.40 (1.73) -0.01 -0.03, 0.01 0.3
Analytic 47 (26) 47 (22) -0.03 -0.26, 0.19 0.8
cogproc 12.2 (5.9) 12.3 (4.9) -0.07 -0.12, -0.02 0.005
prosocial 0.46 (1.14) 0.46 (0.95) 0.00 -0.01, 0.01 0.3
emo_anx 0.09 (0.50) 0.09 (0.39) 0.01 0.00, 0.01 0.001
emo_anger 0.14 (0.64) 0.14 (0.50) 0.00 0.00, 0.01 0.083
emo_sad 0.08 (0.47) 0.08 (0.39) 0.00 0.00, 0.01 0.5
1 Mean (SD)
2 Paired t-test
3 CI = Confidence Interval
Characteristic 1 wk, N = 87,2931 baseline, N = 87,2931 Difference2 95% CI2,3 p-value2
i 3.6 (3.5) 3.6 (3.1) -0.02 -0.05, 0.01 0.2
we 0.64 (1.46) 0.55 (1.16) 0.09 0.08, 0.10 <0.001
they 1.34 (2.05) 1.41 (1.77) -0.06 -0.08, -0.05 <0.001
Analytic 47 (26) 47 (23) 0.24 0.03, 0.46 0.029
cogproc 12.1 (5.8) 12.2 (5.0) -0.11 -0.15, -0.06 <0.001
prosocial 0.47 (1.17) 0.47 (1.00) 0.00 -0.01, 0.01 0.7
emo_anx 0.10 (0.50) 0.09 (0.41) 0.02 0.01, 0.02 <0.001
emo_anger 0.13 (0.58) 0.14 (0.52) -0.01 -0.02, -0.01 <0.001
emo_sad 0.07 (0.44) 0.08 (0.39) 0.00 -0.01, 0.00 0.2
1 Mean (SD)
2 Paired t-test
3 CI = Confidence Interval
Characteristic 2 wk, N = 78,9051 baseline, N = 78,9051 Difference2 95% CI2,3 p-value2
i 3.57 (3.50) 3.59 (3.03) -0.03 -0.06, 0.00 0.071
we 0.57 (1.38) 0.55 (1.14) 0.02 0.01, 0.04 <0.001
they 1.41 (2.10) 1.41 (1.77) 0.00 -0.02, 0.01 0.6
Analytic 47 (26) 47 (22) 0.24 0.02, 0.47 0.034
cogproc 12.2 (5.9) 12.3 (4.9) -0.04 -0.09, 0.02 0.2
prosocial 0.47 (1.17) 0.46 (0.96) 0.01 0.00, 0.02 0.063
emo_anx 0.09 (0.47) 0.09 (0.39) 0.00 0.00, 0.00 0.8
emo_anger 0.14 (0.61) 0.14 (0.51) 0.00 -0.01, 0.00 0.2
emo_sad 0.08 (0.48) 0.08 (0.39) 0.00 0.00, 0.01 0.10
1 Mean (SD)
2 Paired t-test
3 CI = Confidence Interval
Characteristic 3 wk, N = 74,5211 baseline, N = 74,5211 Difference2 95% CI2,3 p-value2
i 3.6 (3.5) 3.6 (3.0) 0.00 -0.03, 0.03
0.9
we 0.55 (1.36) 0.54 (1.12) 0.01 0.00, 0.03 0.028
they 1.42 (2.12) 1.41 (1.75) 0.01 -0.01, 0.03 0.5
Analytic 47 (26) 47 (22) -0.20 -0.43, 0.04 0.10
cogproc 12.3 (5.9) 12.2 (4.9) 0.01 -0.04, 0.07 0.6
prosocial 0.45 (1.15) 0.46 (0.97) -0.01 -0.02, 0.00 0.2
emo_anx 0.09 (0.48) 0.09 (0.39) 0.00 0.00, 0.01 0.3
emo_anger 0.14 (0.62) 0.14 (0.51) 0.00 -0.01, 0.00 0.4
emo_sad 0.08 (0.47) 0.08 (0.38) 0.00 -0.01, 0.00 0.6
1 Mean (SD)
2 Paired t-test
3 CI = Confidence Interval
Characteristic 4 wk, N = 72,8871 baseline, N = 72,8871 Difference2 95% CI2,3 p-value2
i 3.57 (3.49) 3.58 (3.00) -0.01 -0.04, 0.02 0.6
we 0.54 (1.32) 0.54 (1.12) 0.00 -0.02, 0.01 0.6
they 1.41 (2.12) 1.42 (1.75) 0.00 -0.02, 0.02 0.8
Analytic 47 (26) 47 (22) -0.07 -0.31, 0.17 0.6
cogproc 12.3 (5.8) 12.3 (4.9) 0.00 -0.05, 0.05
0.9
prosocial 0.45 (1.14) 0.45 (0.95) 0.00 -0.01, 0.01
0.9
emo_anx 0.08 (0.46) 0.08 (0.38) 0.00 0.00, 0.00
0.9
emo_anger 0.14 (0.61) 0.14 (0.51) 0.00 0.00, 0.01 0.8
emo_sad 0.07 (0.44) 0.08 (0.39) -0.01 -0.01, 0.00 0.004
1 Mean (SD)
2 Paired t-test
3 CI = Confidence Interval
Characteristic 5 wk, N = 72,7991 baseline, N = 72,7991 Difference2 95% CI2,3 p-value2
i 3.54 (3.48) 3.56 (3.00) -0.03 -0.06, 0.00 0.091
we 0.55 (1.36) 0.54 (1.12) 0.01 0.00, 0.03 0.021
they 1.42 (2.12) 1.42 (1.74) 0.01 -0.01, 0.02 0.6
Analytic 47 (26) 47 (22) 0.23 -0.01, 0.46 0.061
cogproc 12.3 (5.9) 12.3 (4.9) 0.04 -0.01, 0.09 0.13
prosocial 0.46 (1.16) 0.45 (0.95) 0.01 0.00, 0.02 0.078
emo_anx 0.09 (0.47) 0.08 (0.38) 0.00 0.00, 0.01 0.5
emo_anger 0.14 (0.61) 0.14 (0.51) 0.00 0.00, 0.01 0.5
emo_sad 0.07 (0.45) 0.07 (0.38) 0.00 -0.01, 0.00 0.3
1 Mean (SD)
2 Paired t-test
3 CI = Confidence Interval
Characteristic 6 wk, N = 64,1561 baseline, N = 64,1561 Difference2 95% CI2,3 p-value2
i 3.56 (3.51) 3.56 (2.98) 0.00 -0.03, 0.04 0.8
we 0.55 (1.36) 0.54 (1.11) 0.01 0.00, 0.02 0.2
they 1.43 (2.14) 1.41 (1.71) 0.02 0.00, 0.04 0.035
Analytic 47 (26) 47 (22) -0.21 -0.46, 0.04 0.10
cogproc 12.3 (5.9) 12.3 (4.9) 0.05 0.00, 0.11 0.060
prosocial 0.45 (1.13) 0.45 (0.94) 0.00 -0.01, 0.01 0.8
emo_anx 0.08 (0.46) 0.08 (0.37) 0.00 0.00, 0.01 0.8
emo_anger 0.14 (0.62) 0.14 (0.49) 0.01 0.00, 0.01 0.079
emo_sad 0.07 (0.45) 0.07 (0.38) 0.00 -0.01, 0.00 0.7
1 Mean (SD)
2 Paired t-test
3 CI = Confidence Interval

Can I see the graphs on your poster again?

Absolutely!